class_docstr: LSTrainer supports GCQ (Gradient Communication Quantization) for distributed multi-machine training
  based on transformers.Trainer.
code: def __init__(self, gcq_args: GCQArguments = None, *args, **kwargs):
      super().__init__(*args, **kwargs)
      logger.setLevel(
          logging.INFO if self.args.should_log else logging.WARN
      )
      self.gcq_args = gcq_args
  
file_path: lightseq/examples/training/huggingface/gcq/ls_hf_gcq_trainer.py
imports: import logging
  import torch
  import torch.distributed
  from transformers import Trainer
  from packaging import version
  from lightseq.training.gcq import GCQState, encode_and_decode
  from examples.training.huggingface.gcq import GCQArguments
label_desc: Use image node_id 1 to create a new LSTrainer object from inherited base classes: Trainer with example: obj = LSTrainer(gcq_args)